Dynamic transcriptional signatures and network responses for clinical symptoms in influenza-infected human subjects using systems biology approaches

New developments in the biology of fibroblast growth factors

The fibroblast growth factor (FGF) family is composed of 18 secreted signaling proteins consisting of canonical FGFs and endocrine FGFs that activate four receptor tyrosine kinases (FGFRs 1-4) and four intracellular proteins (intracellular FGFs or iFGFs) that primarily function to regulate the activity of voltage-gated sodium channels and other molecules. The canonical FGFs, endocrine FGFs, and iFGFs have been reviewed extensively by us and others. In this review, we briefly summarize past reviews and then focus on new developments in the FGF field since our last review in 2015. Some of the highlights in the past 6 years include the use of optogenetic tools, viral vectors, and inducible transgenes to experimentally modulate FGF signaling, the clinical use of small molecule FGFR inhibitors, an expanded understanding of endocrine FGF signaling, functions for FGF signaling in stem cell pluripotency and differentiation, roles for FGF signaling in tissue homeostasis and regeneration, a continuing elaboration of mechanisms of FGF signaling in development, and an expanding appreciation of roles for FGF signaling in neuropsychiatric diseases. This article is categorized under: Cardiovascular Diseases > Molecular and Cellular Physiology Neurological Diseases > Molecular and Cellular Physiology Congenital Diseases > Stem Cells and Development Cancer > Stem Cells and Development.

Fibroblast growth factor-9 expression in airway epithelial cells amplifies the type I interferon response and alters influenza A virus pathogenesis

Influenza A virus (IAV) preferentially infects conducting airway and alveolar epithelial cells in the lung. The outcome of these infections is impacted by the host response, including the production of various cytokines, chemokines, and growth factors. Fibroblast growth factor-9 (FGF9) is required for lung development, can display antiviral activity in vitro, and is upregulated in asymptomatic patients during early IAV infection. We therefore hypothesized that FGF9 would protect the lungs from respiratory virus infection and evaluated IAV pathogenesis in mice that overexpress FGF9 in club cells in the conducting airway epithelium (FGF9-OE mice). However, we found that FGF9-OE mice were highly susceptible to IAV and Sendai virus infection compared to control mice. FGF9-OE mice displayed elevated and persistent viral loads, increased expression of cytokines and chemokines, and increased numbers of infiltrating immune cells as early as 1 day post-infection (dpi). Gene expression analysis showed an elevated type I interferon (IFN) signature in the conducting airway epithelium and analysis of IAV tropism uncovered a dramatic shift in infection from the conducting airway epithelium to the alveolar epithelium in FGF9-OE lungs. These results demonstrate that FGF9 signaling primes the conducting airway epithelium to rapidly induce a localized IFN and proinflammatory cytokine response during viral infection. Although this response protects the airway epithelial cells from IAV infection, it allows for early and enhanced infection of the alveolar epithelium, ultimately leading to increased morbidity and mortality. Our study illuminates a novel role for FGF9 in regulating respiratory virus infection and pathogenesis.

Influenza viruses are respiratory viruses that cause significant morbidity and mortality worldwide. In the lungs, influenza A virus primarily infects epithelial cells that line the conducting airways and alveoli. Fibroblast growth factor-9 (FGF9) is a growth factor that has been shown to have antiviral activity and is upregulated during early IAV infection in asymptomatic patients, leading us to hypothesize that FGF9 would protect the lung epithelium from IAV infection. However, mice that express and secrete FGF9 from club cells in the conducting airway had more severe respiratory virus infection and a hyperactive inflammatory immune response as early as 1 day post-infection. Analysis of the FGF9-expressing airway epithelial cells found an elevated antiviral and inflammatory interferon signature, which protected these cells from severe IAV infection. However, heightened infection of alveolar cells resulted in excessive inflammation in the alveoli, resulting in more severe disease and death. Our study identifies a novel antiviral and inflammatory role for FGFs in the lung airway epithelium and confirms that early and robust IAV infection of alveolar cells results in more severe disease.

Evaluation of botanicals as potential COVID-19 symptoms terminator

Information about the coronavirus disease 2019 (COVID-19) pandemic is still evolving since its appearance in December 2019 and has affected the whole world. Particularly, a search for an effective and safe treatment for COVID-19 continues. Botanical mixtures contain secondary metabolites (such as flavonoids, phenolics, alkaloids, essential oils etc.) with many therapeutic effects. In this study, the use of herbal treatments against COVID-19 was evaluated. Medical synthetic drugs focus mainly on respiratory symptoms, however herbal therapy with plant extracts may be useful to relieve overall symptoms of COVID-19 due to the variety of bioactive ingredients. Since COVID-19 is a virus that affects the respiratory tract, the antiviral effects of botanicals/plants against respiratory viruses have been examined through clinical studies. Data about COVID-19 patients revealed that the virus not only affects the respiratory system but different organs including the gastrointestinal (GI) system. As GI symptoms seriously affect quality of life, herbal options that might eliminate these problems were also evaluated. Finally, computer modeling studies of plants and their active compounds on COVID-19 were included. In summary, herbal therapies were identified as potential options for both antiviral effects and control of COVID-19 symptoms. Further data will be needed to enlighten all aspects of COVID-19 pathogenesis, before determining the effects of plants on severe acute respiratory syndrome coronavirus 2.

An informatics research platform to make public gene expression time-course datasets reusable for more scientific discoveries

The exponential growth of genomic/genetic data in the era of Big Data demands new solutions for making these data findable, accessible, interoperable and reusable. In this article, we present a web-based platform named Gene Expression Time-Course Research (GETc) Platform that enables the discovery and visualization of time-course gene expression data and analytical results from the NIH/NCBI-sponsored Gene Expression Omnibus (GEO). The analytical results are produced from an analytic pipeline based on the ordinary differential equation model. Furthermore, in order to extract scientific insights from these results and disseminate the scientific findings, close and efficient collaborations between domain-specific experts from biomedical and scientific fields and data scientists is required. Therefore, GETc provides several recommendation functions and tools to facilitate effective collaborations. GETc platform is a very useful tool for researchers from the biomedical genomics community to present and communicate large numbers of analysis results from GEO. It is generalizable and broadly applicable across different biomedical research areas. GETc is a user-friendly and efficient web-based platform freely accessible at http://genestudy.org/.

Investigation of temporal and spatial heterogeneities of the immune responses to Bordetella pertussis infection in the lung and spleen of mice via analysis and modeling of dynamic microarray gene expression data

Bordetella pertussis (B. pertussis) is the causative agent of pertussis, also referenced as whooping cough. Although pertussis has been appropriately controlled by routine immunization of infants, it has experienced a resurgence since the beginning of the 21st century. Given that elucidating the immune response to pertussis is a crucial factor to improve therapeutic and preventive treatments, we re-analyzed a time course microarray dataset of B. pertussis infection by applying a newly developed dynamic data analysis pipeline. Our results indicate that the immune response to B. pertussis is highly dynamic and heterologous across different organs during infection. Th1 and Th17 cells, which are two critical types of T helper cell populations in the immune response to B. pertussis, and follicular T helper cells (TFHs), which are also essential for generating antibodies, might be generated at different time points and distinct locations after infection. This phenomenon may indicate that different lymphoid organs may have their unique functions during infection. These findings provide a better understanding of the basic immunology of bacterial infection, which may provide valuable insights for the improvement of pertussis vaccine design in the future.

Restructured GEO: restructuring Gene Expression Omnibus metadata for genome dynamics analysis

Motivation

Gene Expression Omnibus (GEO) and other publicly available data store their metadata in the format of unstructured English text, which is very difficult for automated reuse.

Results

We employed text mining techniques to analyze the metadata of GEO and developed Restructured GEO database (ReGEO). ReGEO reorganizes and categorizes GEO series and makes them searchable by two new attributes extracted automatically from each series' metadata. These attributes are the number of time points tested in the experiment and the disease being investigated. ReGEO also makes series searchable by other attributes available in GEO, such as platform organism, experiment type, associated PubMed ID as well as general keywords in the study's description. Our approach greatly expands the usability of GEO data, demonstrating a credible approach to improve the utility of vast amount of publicly available data in the era of Big Data research.

The Antibody-Secreting Cell Response to Infection: Kinetics and Clinical Applications

Despite the availability of advances in molecular diagnostic testing for infectious disease, there is still a need for tools that advance clinical care and public health. Current methods focus on pathogen detection with unprecedented precision, but often lack specificity. In contrast, the host immune response is highly specific for the infecting pathogen. Serological studies are rarely helpful in clinical settings, as they require acute and convalescent antibody testing. However, the B cell response is much more rapid and short-lived, making it an optimal target for determining disease aetiology in patients with infections. The performance of tests that aim to detect circulating antigen-specific antibody-secreting cells (ASCs) has previously been unclear. Test performance is reliant on detecting the presence of ASCs in the peripheral blood. As such, the kinetics of the ASC response to infection, the antigen specificity of the ASC response, and the methods of ASC detection are all critical. In this review, we summarize previous studies that have used techniques to enumerate ASCs during infection. We describe the emergence, peak, and waning of these cells in peripheral blood during infection with a number of bacterial and viral pathogens, as well as malaria infection. We find that the timing of antigen-specific ASC appearance and disappearance is highly conserved across pathogens, with a peak response between day 7 and day 8 of illness and largely absent following day 14 since onset of symptoms. Data show a sensitivity of ~90% and specificity >80% for pathogen detection using ASC-based methods. Overall, the summarised work indicates that ASC-based methods may be very sensitive and highly specific for determining the etiology of infection and have some advantages over current methods. Important areas of research remain, including more accurate definition of the timing of the ASC response to infection, the biological mechanisms underlying variability in its magnitude and the evolution and the B cell receptor in response to immune challenge. Nonetheless, there is potential of the ASC response to infection to be exploited as the basis for novel diagnostic tests to inform clinical care and public health priorities.

Controllability and stability analysis of large transcriptomic dynamic systems for host response to influenza infection in human

Background

Gene regulatory networks are complex dynamic systems and the reverse-engineering of such networks from high-dimensional time course transcriptomic data have attracted researchers from various fields. It is also interesting and important to study the behavior of the reconstructed networks on the basis of dynamic models and the biological mechanisms. We focus on the gene regulatory networks reconstructed using the ordinary differential equation (ODE) modelling approach and investigate the properties of these networks.

Results

Controllability and stability analyses are conducted for the reconstructed gene response networks of 17 influenza infected subjects based on ODE models. Symptomatic subjects tend to have larger numbers of driver nodes, higher proportions of critical links and lower proportions of redundant links than asymptomatic subjects. We also show that the degree distribution, rather than the structure of networks, plays an important role in controlling the network in response to influenza infection. In addition, we find that the stability of high-dimensional networks is very sensitive to randomness in the reconstructed systems brought by errors in measurements and parameter estimation.

Conclusions

The gene response networks of asymptomatic subjects are easier to be controlled than those of symptomatic subjects. This may indicate that the regulatory systems of asymptomatic subjects are easier to recover from disease stimulations, so these subjects are less likely to develop symptoms. Our results also suggest that stability constraint should be considered in the modelling of high-dimensional networks and the estimation of network parameters.

Correlation-based iterative clustering methods for time course data: The identification of temporal gene response modules for influenza infection in humans

Many pragmatic clustering methods have been developed to group data vectors or objects into clusters so that the objects in one cluster are very similar and objects in different clusters are distinct based on some similarity measure. The availability of time course data has motivated researchers to develop methods, such as mixture and mixed-effects modelling approaches, that incorporate the temporal information contained in the shape of the trajectory of the data. However, there is still a need for the development of time-course clustering methods that can adequately deal with inhomogeneous clusters (some clusters are quite large and others are quite small). Here we propose two such methods, hierarchical clustering (IHC) and iterative pairwise-correlation clustering (IPC). We evaluate and compare the proposed methods to the Markov Cluster Algorithm (MCL) and the generalised mixed-effects model (GMM) using simulation studies and an application to a time course gene expression data set from a study containing human subjects who were challenged by a live influenza virus. We identify four types of temporal gene response modules to influenza infection in humans, i.e., single-gene modules (SGM), small-size modules (SSM), medium-size modules (MSM) and large-size modules (LSM). The LSM contain genes that perform various fundamental biological functions that are consistent across subjects. The SSM and SGM contain genes that perform either different or similar biological functions that have complex temporal responses to the virus and are unique to each subject. We show that the temporal response of the genes in the LSM have either simple patterns with a single peak or trough a consequence of the transient stimuli sustained or state-transitioning patterns pertaining to developmental cues and that these modules can differentiate the severity of disease outcomes. Additionally, the size of gene response modules follows a power-law distribution with a consistent exponent across all subjects, which reveals the presence of universality in the underlying biological principles that generated these modules.